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TransETA: transformer networks for estimated time of arrival with local congestion representation

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Abstract

Estimated time of arrival (ETA) is an estimate of the vehicle travel time from the origin to destination in the roadworks. From the perspective of travel planning or resource allocation, accurate ETA is significantly important. In recent years, deep learning-based methods represented by recurrent neural networks has been widely used in travel time prediction tasks, but such methods cannot effectively learn data association at different moments. At the same time, the existing methods do not effectively leverage local traffic information. Targeting these challenges, this paper proposes a new model TransETA to predict vehicle travel time. The model includes three modules: the input feature transformation module uses graph convolutional network (GCN) to extract the local congestion feature, the deep forest module mainly deals with static trajectory data, and ETA-Transformer module processes the feature extraction of dynamic trajectory data. Finally, we conducted experiments on two large trajectory datasets. The experimental results show that the proposed hybrid deep learning method, TransETA, outperforms the state-of-the-art models. On the Chengdu and Porto datasets, our proposed method shows an improvement of 6s and 9s in mean absolute error compared to the current best performing method, respectively. Also the average absolute percentage error is reduced by 2.34% and 3.64% respectively. The effectiveness of each module was approved through ablation experiments. Specifically, local congestion information representation can effectively improve the accuracy of the prediction. ETA-Transformer module is more effective in extracting spatio-temporal feature correlation than the LSTM-based method.

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References

  1. Gers FA, Eck D, Schmidhuber J (2002) Applying lstm to time series predictable through time-window approaches, 193–200

  2. Zhang GP (2003) Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  3. Contreras J, Espinola R, Nogales FJ, Conejo AJ (2003) Arima models to predict next-day electricity prices. IEEE Trans Power Syst 18(3):1014–1020

    Article  Google Scholar 

  4. Akaike H (1969) Fitting autoregressive models for prediction. Ann Inst Stat Math 21(1):243–247

    Article  MathSciNet  Google Scholar 

  5. Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5–10

    Article  Google Scholar 

  6. Gustafsson B, Kreiss H-O, Oliger J (1995) Time dependent problems and difference methods, 24

  7. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory, pp 144–152

  8. Quinlan JR (2014) C4. 5: programs for machine learning

  9. Qiu J, Wu Q, Ding G, Xu Y (2016) Feng S (2016) A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing 1:1–16

    Google Scholar 

  10. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining, pp 785–794

  11. Ruck DW, Rogers SK, Kabrisky M (1990) Feature selection using a multilayer perceptron. Journal of Neural Network Computing 2(2):40–48

    Google Scholar 

  12. Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211

    Article  Google Scholar 

  13. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  14. Yang B, Guo C, Jensen CS (2013) Travel cost inference from sparse, spatio temporally correlated time series using markov models. Proceedings of the VLDB Endowment 6(9):769–780

    Article  Google Scholar 

  15. Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 25–34

  16. Babaei A, Khedmati M, Jokar MRA, Tirkolaee EB (2023) Sustainable transportation planning considering traffic congestion and uncertain conditions. Expert Syst Appl 227:119792. https://doi.org/10.1016/j.eswa.2023.119792

    Article  Google Scholar 

  17. Jenelius E, Koutsopoulos HN (2013) Travel time estimation for urban road networks using low frequency probe vehicle data. Transport Res Part B: Methodological 53:64–81

    Article  Google Scholar 

  18. Hofleitner A, Herring R, Abbeel P, Bayen A (2012) Learning the dynamics of arterial traffic from probe data using a dynamic bayesian network. IEEE Trans Intell Transp Syst 13(4):1679–1693

    Article  Google Scholar 

  19. Zhan X, Hasan S, Ukkusuri SV, Kamga C (2013) Urban link travel time estimation using large-scale taxi data with partial information. Transport Res Part C: Emerg Technol 33:37–49

    Article  Google Scholar 

  20. Zhang F, Zhu X, Hu T, Guo W, Chen C, Liu L (2016) Urban link travel time prediction based on a gradient boosting method considering spatiotemporal correlations. ISPRS Int J Geo Inf 5(11):201

    Article  Google Scholar 

  21. Wang H, Kuo YH, Kifer D, Li Z (2016) A simple baseline for travel time estimation using large-scale trip data. In: 24th ACM SIGSPATIAL International conference on advances in geographic information systems, ACM SIGSPATIAL GIS 2016, p 61. Association for Computing Machinery

  22. Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2022) Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans Intell Transp Syst 23(6):4927–4943

  23. Li Y, Fu K, Wang Z, Shahabi C, Ye J, Liu Y (2018) Multi-task representation learning for travel time estimation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1695–1704

  24. Wang D, Zhang J, Cao W, Li J, Zheng Y (2018) When will you arrive? estimating travel time based on deep neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  25. Jindal I, Chen X, Nokleby M, Ye J et al (2017) A unified neural network approach for estimating travel time and distance for a taxi trip. arXiv:1710.04350

  26. Wang Z, Fu K, Ye J (2018) Learning to estimate the travel time. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 858–866

  27. Woodard D, Nogin G, Koch P, Racz D, Goldszmidt M, Horvitz E (2017) Predicting travel time reliability using mobile phone gps data. Transport Res Part C: Emerg Technol 75:30–44

    Article  Google Scholar 

  28. Lan W, Xu Y, Zhao B (2019) Travel time estimation without road networks: an urban morphological layout representation approach. IJCAI, 1772–1778

  29. Lin X, Wang Y, Xiao X, Li Z, Bhowmick SS (2019) Path travel time estimation using attribute-related hybrid trajectories network. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1973–1982

  30. Li X, Cong G, Sun A, Cheng Y (2019) Learning travel time distributions with deep generative model. In: The World Wide Web conference, pp 1017–1027

  31. Dai R, Xu S, Gu Q, Ji C, Liu K (2020) Hybrid spatio-temporal graph convolutional network: improving traffic prediction with navigation data. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3074–3082

  32. Sun J, Kim J (2021) Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networks. Transport Res Part C: Emerg Technol 128:103–114

    Article  Google Scholar 

  33. Civilis A, Jensen CS, Pakalnis S (2005) Techniques for efficient road-network-based tracking of moving objects. IEEE Trans Knowl Data Eng 17(5):698–712

    Article  Google Scholar 

  34. Chawathe SS (2007) Segment-based map matching. In: 2007 IEEE Intelligent vehicles symposium, IEEE, pp 1190–1197

  35. Alt H, Efrat A, Rote G, Wenk C (2003) Matching planar maps. J Algorithms 49(2):262–283

    Google Scholar 

  36. Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: Proceedings of the 31st international conference on very large data bases, pp 853–864

  37. Lou Y, Zhang C, Zheng Y, Xie X, Wang W, Huang Y (2009) Map-matching for low-sampling-rate gps trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 352–361

  38. Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv:1312.6203

  39. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. arXiv:1606.09375

  40. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907

  41. Atwood J, Towsley D (2015) Diffusion-convolutional neural networks. arXiv:1511.02136

  42. Niepert M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs. In: International conference on machine learning, PMLR, pp 2014–2023

  43. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv:1710.10903

  44. Zhou Z-H, Feng J (2017) Deep forest. arXiv:1702.08835

  45. Duan Y, Yisheng L, Wang F-Y (2016) Travel time prediction with lstm neural network. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), IEEE, pp 1053–1058

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Acknowledgements

The research is supported by the National Key R &D Program of China (2018YFB1600500), the National Science Foundation of China (61673366, 61620106009, 62102258), the European COST Action TU1102, the Shanghai Pujiang Program (21PJ1407300) and the Fundamental Research Funds for the Central Universities. We appreciate the valuable insights and significant contributions provided by Hu Hui Feng in the paper revision.

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Authors and Affiliations

  1. School of Computer Science Technology, University of Chinese Academy of Sciences, Beijing, 101408, China

    Shu Lin, Shengjian Zhao & Jungang Xu

  2. The MoE Key Laboratory of Artificial Intelligence in AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanyan Xu

  3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China

    Yibing Wang

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  1. Shu Lin
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  2. Yanyan Xu
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  3. Shengjian Zhao
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  4. Yibing Wang
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  5. Jungang Xu
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Lin, S., Xu, Y., Zhao, S. et al. TransETA: transformer networks for estimated time of arrival with local congestion representation. Appl Intell 53, 30384–30399 (2023). https://doi.org/10.1007/s10489-023-05139-6

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